SOTAVerified

Super-Resolution

Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.

( Credit: MemNet )

Papers

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Showing 3741–3750 of 3874 papers

TitleStatusHype
Beyond Deep Residual Learning for Image Restoration: Persistent Homology-Guided Manifold SimplificationCode0
Normalizing the Normalizers: Comparing and Extending Network Normalization Schemes—0
Optimal Surface Segmentation with Convex Priors in Irregularly Sampled Space—0
The Little Engine that Could: Regularization by Denoising (RED)Code0
Super-resolution estimation of cyclic arrival rates—0
Compressive Holographic Video—0
Amortised MAP Inference for Image Super-resolution—0
Sparsity-based Color Image Super Resolution via Exploiting Cross Channel Constraints—0
Blind Facial Image Quality Enhancement using Non-Rigid Semantic Patches—0
Wavelet-Based Segmentation on the Sphere—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1super-resolutionAverage PSNR20.41—Unverified